As AI reshapes global finance, institutions face a choice: adapting to divergent regional ecosystems or pursuing a unified approach. European regulatory ambition, American disruptive innovation, and Asian pragmatism each embody competing visions of progress. How can efficiency, compliance, and sovereignty be balanced in such a fragmented landscape?
Three continents, three philosophies
At global level, AI is shaping finance through logics rooted in regional contexts. In Europe, the priority is regulation and transparency, with strict requirements imposed by the AI Act. This approach encourages institutions to prioritise explainable AI, particularly for credit scoring and fraud detection. Despite over €150bn invested in 2024 according to IDC, concrete results have yet to emerge – reflecting a transformation that is slow yet rigorous.
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Across the Atlantic, North America is banking on rapid innovation, with AI adoption at scale. JP Morgan, for example, has deployed AI assistants for 140,000 employees. However, this momentum is held back by regulatory fragmentation across states, making it difficult to reach any form of maturity.
In Asia, a pragmatic approach prevails, with advanced adoption for use cases such as multilingual chatbots and fraud prevention. The rise of open source, exemplified by DeepSeek, along with balanced governance frameworks foster innovation while maintaining ethical oversight. In Hong Kong, 75% of financial institutions are already experimenting with AI, according to its Institute for Monetary and Financial Research, while Singapore stands out with a framework that balances innovation and ethical oversight.
Adopting a “glocal” approach
Navigating these philosophies requires a balance between regulation, innovation, and performance. Institutions must rely on modular architectures capable of adapting to diverse regulatory and operational environments. This means ensuring data sovereignty in line with local requirements. Interoperability is becoming essential to adapt solutions to each market while maintaining global consistency.
Taking regional specificities into account requires local skills centres capable of addressing specific needs, such as language processing for chatbots. This glocal approach must go along with increased transparency: systematic documentation of data sources, identification of potential biases, and traceability of algorithmic decisions, especially for sensitive use cases such as granting credit or monitoring risky transactions.
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By GlobalDataFaced with the demand for trust, management must also strengthen their ethical culture by training themselves on new regulatory issues and integrating real-time monitoring tools. To facilitate this agility, it is crucial to structure an open ecosystem, based on regional partnerships and the adoption of a hybrid cloud: non-sensitive data is entrusted to public infrastructures and the most critical data is protected on private servers.
The battle for AI will be won neither through forced standardisation nor excessive regionalism. Institutions must cultivate a strategic triad: an ethical foundation aligned with strict European standards, technological agility inspired by the American model, and the operational pragmatism drawn from Asia. Those who transform the current fragmentation into a distributed innovation lab will unlock the ultimate value of AI: not as a tool for disruption, but as a lever for geostrategic resilience.
Jamil Jiva is Global Head of Asset Management for Linedata
